FutureSmart AI Demos

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It discloses the entire output structure (winner, scores, conditions, dissenting links, tie-break reason, proof URLs), deterministic behavior ('same question returns the same answer'), and the refusal condition ('coverage: not_tested'). This is comprehensive and transparent for a read-only query tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but every sentence earns its place: purpose, return value detail, determinism, refusal behavior, and routing guidance. It is front-loaded with the main purpose and flows logically. No fluff or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool is complex (4 params, no output schema) and the description fully explains what the tool returns, including edge cases (refusal when not tested) and the conditions under which results hold. It also names a sibling for further raw data. This is complete for an agent to select and invoke correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already describes all 4 parameters with 100% coverage, so the baseline is 3. The description adds the 'Alternative: pass ranking + criterion explicitly' note, which is useful but does not significantly go below or above baseline. No additional syntax or format details are given beyond what the schema provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'ANSWER a buyer question in ONE call: which tool is best at a specific capability, with proof.' It also differentiates from siblings by explicitly naming 'get_evidence' as an alternative for raw cells. The verb 'ANSWER' and the resource (buyer question about tool capability) are specific and unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit usage guidance: 'Start HERE for any 'which tool is best at X' / 'A or B for X' question; use get_evidence for the raw cells behind it.' This gives both when to use and when not to use, naming a specific alternative. It also states the tool's honest refusal behavior when coverage is not tested.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description discloses that comparisons are built from real observation cells with artifacts, not prose, and notes the structural honesty enforcement. Since no annotations are provided, the description carries the full burden and does so well.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is informative and front-loaded, but slightly dense. It efficiently conveys key points without wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description adequately explains the output structure (head-to-head, related_not_same_input, unique evidence). It provides sufficient context for the agent to understand what the tool returns.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 100% schema description coverage, the schema already documents parameters clearly. The description adds no additional meaning beyond what the schema provides, so baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool does an 'evidence-aligned comparison of two tools' and specifies the structural honesty mechanism, differentiating it from sibling tools like get_evidence or get_ranking.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for comparing two tools but does not explicitly state when to use this vs. alternatives, nor does it mention when not to use it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It accurately describes the output (verdict, score, note, artifacts) and explains the three evidence states. It does not explicitly state read-only behavior or potential side effects, but the nature of a query tool and the output description strongly imply no mutations. The description is transparent about what the tool returns and its filtering capabilities.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, well-structured paragraph that front-loads the primary action ('Query the evidence graph') and then details the output and filter options. Every sentence is informative with no wasted words. It is concise yet comprehensive.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 8 parameters, no output schema, and no annotations, the description provides a good level of completeness: it describes the output (verdict, score, note, artifacts), explains evidence states, and lists all filterable fields. It lacks details on output format or pagination beyond the limit parameter, but is sufficient for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema covers 75% of parameters with descriptions. The tool description adds extra meaning by explaining the evidence_state values ('verified', 'observed', 'scored-only') and reinforcing the filterable fields. This goes beyond the schema's enum definitions, adding semantic clarity for the agent.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool queries the evidence graph and returns observation cells with verdict, score, note, and artifacts. It lists the filterable dimensions (tool, scenario, criterion, verdict, evidence state) and explains the meaning of evidence_state values, making the purpose very specific and distinct from sibling tools like get_ranking or compare_tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains what the tool does and its filters, but does not explicitly state when to use it versus alternatives (e.g., get_ranking for summary, tools_in_ranking for listing). It implies it's for detailed evidence behind rankings, but lacks explicit when-to-use or when-not-to-use guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It clearly implies a read operation by stating 'return everything tagged with it' and describes the output structure. It doesn't explicitly confirm read-only or mention rate limits, but the 'mirrors a persona landing page' adds behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences with no redundant words. The main action is front-loaded, and the structure is highly efficient, earning a top score.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool returns multiple related entities, and the description lists them all (persona, use_cases, rankings, etc.). Since there is no output schema, the description adequately informs the agent of the return structure. It doesn't cover pagination or sorting, but the scope is reasonable for this aggregated tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% (slug described in schema). The description adds value by specifying that the slug comes from list_personas and provides an example ('students'), which aids correct parameter usage beyond the schema's brief description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('return') and resource ('everything tagged with persona slug'), clearly listing all returned components (persona, use_cases, rankings, etc.). It distinguishes itself from sibling tools like list_personas (list all) and get_tool (single tool) by indicating it aggregates multiple related entities.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states to obtain the slug from list_personas, providing clear context for when to use this tool. It doesn't discuss when not to use it or alternatives like get_use_case for individual components, but the instruction is sufficient for correct invocations.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full burden and does so thoroughly. It discloses the response format, proof behavior (sample/full/none), that counts are true totals in every mode, and null fallback. It also explains the fields projection for token efficiency, leaving little ambiguity about tool behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the main purpose and is dense with useful details. The proof explanation is lengthy but each sentence contributes value. It could be slightly tighter, but it remains well-structured and efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Without an output schema, the description compensates by explaining the JSON+Markdown envelope, proof artifact behavior, true totals, and fields projection. For a tool with 3 parameters and nested proof structure, this is complete enough for an agent to invoke correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with detailed parameter descriptions for slug, proof, and fields. The description adds only a token-efficiency tip for fields, which is a usage suggestion rather than new semantic meaning. Thus, baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns "Full ranking detail as a JSON+Markdown envelope" with ranked tools, criteria, winner, breakdown, and verdicts. It distinguishes from siblings by detailing that each ranked tool carries proof with artifact URLs and evidence permalinks, which is unique to this tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use this tool: when you need full ranking detail with evidence. It also hints at an alternative by saying "no second call needed to cite evidence," implying you can skip get_evidence. However, it does not explicitly compare to similar tools like list_rankings or tools_in_ranking.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden and does well: it explains the output envelope, proof modes, true artifact counts, null behavior for unknown proof, and that identity fields are always included. It does not explicitly state read-only/authorization expectations, but this is implicitly a fetch operation, and the disclosed behaviors are substantial.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but each sentence earns its place: the first defines the envelope, the second explains proof artifacts and count behavior, the third explains field projection and token efficiency. It is well-structured with backticks and lists, not bloated.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description thoroughly covers the return structure (JSON+Markdown envelope), proof behavior, counts, projection, and always-included identity keys. This gives an agent a complete mental model of the response before invoking the tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already has 100% coverage, but the description adds value beyond it: 'fields' is explained as token-efficient projection, identity keys are always included, and 'proof' modes are described with behavior for counts and spread. This is more than the schema alone provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns 'Full tool detail' and enumerates the exact contents (identity, pricing, per-feature scores, fit, FAQ, relationships, our_take, in-depth review, proof). This distinguishes it from sibling tools like list_tools and compare_tools, which serve different purposes (listing and comparison).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use the tool (fetching full details for a single tool) and hints at a workflow by noting the slug comes 'from list_tools'. However, it does not explicitly name alternatives or exclusions (e.g., when to use get_evidence instead of inline proof), so it stops short of a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It discloses return format (JSON+Markdown), null handling, and projection behavior. It does not mention permissions or side effects, but for a read-like retrieval tool, this is adequate. The description adds value beyond immediate expectations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences: first defines purpose and output format, second provides usage guidance and null case. No extraneous words, front-loaded with key information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (two parameters, no output schema, no annotations), the description adequately covers the return format, null handling, and projection option. It is sufficient for an agent to correctly invoke and interpret the tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, providing baseline clarity for both parameters. The description adds the concept of 'token-efficiency' for the 'fields' parameter and notes that identity fields are always included, which goes beyond the schema's enum listing.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it returns full use-case detail as a JSON+Markdown envelope, including step guide, pros/cons, FAQ, and tools used. It distinguishes from siblings like 'list_use_cases' (which lists) and 'get_tool' (which gets tool details) by specifying it retrieves a single use case with comprehensive content.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description advises using the 'fields' parameter for token-efficient projection, which provides clear guidance on when to customize the response. However, it does not explicitly contrast with alternative tools or state when not to use this tool, though the context of retrieving full detail versus listing is implied.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It discloses the output content but does not mention side effects, access requirements, or that it is read-only. Adequate but could be more explicit.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Single, efficient sentence (20 words) that front-loads the main action and key output details. No wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a parameterless listing tool with no output schema, the description adequately covers what the tool does and the key aspects of its output (counts, source flag, sorting). Complete for its complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

There are no parameters, so schema coverage is 100%. Baseline score of 4 applies; no additional parameter info needed or provided.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states that the tool lists category vocabulary with per-type counts and a source flag, sorted by total. This specific verb+resource combination distinguishes it from sibling list_* tools that list other entities.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool versus alternatives like list_tools or list_use_cases. Usage is implied by the name but not clarified.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, yet description only lists returned fields. Does not disclose behavioral traits like ordering (likely by updated_at), whether results are paginated (schema covers offset/limit but description doesn't state it's a list), or if any side effects exist. For a read-only tool, minimal but acceptable given schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Single sentence, concisely lists purpose and key output fields. Front-loaded with verb and resource. Could improve with structured bullets but highly efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, description enumerates all returned properties, compensating adequately. Schema fully describes parameters. Lacks ordering info but overall complete for a list tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with clear descriptions for limit and offset. Description adds no additional meaning beyond the schema, so baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description uses specific verb 'List' and resource 'published head-to-head comparison pages', enumerating exact fields. Distinguishes from sibling tools like compare_tools (likely creation) and get_evidence (single comparison).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Implies usage for listing all comparisons, but no explicit when-to-use vs alternatives. Sibling tools like compare_tools suggest alternative uses (creating), but no guidance on filtering or exclusion criteria.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must disclose all behavioral traits. It mentions output counts and slug usage, but fails to state whether the tool is read-only, whether authentication is needed, or any rate limits or destructive potential.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two concise sentences with no wasted words. First sentence states purpose and output, second provides a useful link to another tool. Highly efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with no parameters and no output schema, the description adequately states purpose and output fields. However, it lacks detail on the return format (e.g., structure of the list, whether it's a single object or array) and could elaborate on the meaning of 'published-page counts'.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has no parameters, and schema description coverage is 100%. Baseline for 0 parameters is 4; the description adds no additional meaning but is not required to.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool lists personas with counts per type, and mentions slugs are valid for get_persona. It distinguishes from siblings (other list tools) implicitly, but without explicit differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for using the tool: to get a list with counts per type. However, it does not specify when not to use it or mention alternative tools, though it hints at a workflow with get_persona.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden for behavioral transparency. It only lists returned fields, omitting important aspects like pagination behavior (beyond limit/offset), ordering, read-only nature, or any default filters. This is insufficient for a listing tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence that gets the point across. The field list is somewhat verbose but acceptable for a tool with no output schema. It is front-loaded with the action and resource.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The field list compensates slightly for the missing output schema, but the description lacks information on ordering, filtering options, or whether the list is exhaustive. For a simple listing tool, it is minimally adequate but not complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% coverage for both parameters ('limit' and 'offset') with descriptions already stating 'omit for all' and 'for paging'. The tool description adds no additional meaning beyond the schema, so baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool lists published ranking pages and enumerates the fields returned. It uses a specific verb ('List') and resource ('published ranking pages'), distinguishing it from siblings like 'get_ranking' which likely returns a single ranking.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives. It does not mention any conditions, exclusions, or comparisons to sibling tools like 'rankings_for_tool' or 'tools_in_ranking'. The agent has no help in deciding which tool to invoke.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so the description fully carries the burden. It indicates a read operation but lacks details on pagination behavior, rate limits, or authentication requirements. The output fields are listed, but response structure (array, order) is omitted.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single concise sentence (10 words) that covers purpose and output fields. It is efficient but could be slightly more structured (e.g., mentioning it is a safe read operation).

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with no annotations and no output schema, the description is barely adequate. It lists output fields but does not describe the full response format (e.g., array of objects), default ordering, or pagination behavior. Important behavioral context is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and both parameters (limit, offset) are well-described in the schema. The description adds no extra meaning beyond listing output fields, so baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool lists published toolkit pages (curated bundles) and specifies the returned fields: id, title, slug, url, category. This distinguishes it from sibling tools like list_tools (individual tools) and list_categories (categories alone).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for listing curated toolkit pages but does not explicitly state when to use this tool over siblings like list_tools or list_categories. No exclusions or alternatives are mentioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description must cover behavior. It states it lists published tool pages but does not disclose that it returns all items if limit omitted, or that it's read-only. No mention of ordering, pagination behavior, or any side effects. Minimal behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Single sentence, clear, front-loaded with verb and resource, and efficiently lists returned fields. No wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a list tool with no output schema, description lists fields which is helpful. However, lacks details on default behavior (e.g., what happens if limit omitted), pagination instructions for offset parameter, and whether results are ordered. Adequate but incomplete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% (both parameters described). Description does not add additional meaning beyond schema. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description explicitly states verb 'List', resource 'published AI tool pages', and the exact fields returned (id, name, slug, url, domain, personas[], categories[]). This distinguishes it from sibling tools like 'get_tool' (single tool) and 'search' (full-text).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives like 'search' or 'get_tool'. Does not specify constraints like maximum returned items (though limit max is in schema) or that it returns all items by default. No when-not-to-use advice.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full burden. It mentions pagination (limit/offset) and return fields but does not disclose sorting, ordering, or potential side effects. Adequate but not exhaustive for a list operation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Single, concise sentence with no superfluous words. All information is front-loaded and every part serves a purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description specifies the return fields and pagination. It clarifies the scope ('published use-case pages') which helps context. Missing details like default ordering or rate limits, but sufficient for a simple list tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with descriptions for both parameters. The tool description does not add additional meaning beyond the schema; it merely lists return fields. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states the tool lists published use-case pages and enumerates the fields returned (id, title, slug, url, persona, category, updated_at). The verb 'list' and resource 'use_cases' are specific, and the parenthetical '(how-to guides)' adds context distinguishing it from other list tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives like 'search' or 'get_use_case'. The description does not specify the intended audience or context, leaving the agent to infer usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full behavioral burden. It discloses what the tool returns (tool info and rankings), but it does not mention any side effects, permissions, rate limits, or error behavior (e.g., handling invalid tool_id). The description is purely output-focused. For a tool with no annotations, this is insufficient for full behavioral transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence that efficiently conveys the core behavior: input a tool id, output tool info and rankings. It front-loads the key action ('Given a tool id, return...'). However, it could be slightly improved by separating input and output specification more clearly. Overall, it is concise with no superfluous words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (one parameter, no output schema), the description provides the essential structure of the return value. However, it lacks information about error handling (e.g., what if the tool id is invalid?), pagination (rankings could be many), or data freshness. For a minimal retrieval tool, it is adequate but not fully complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

There is only one parameter, tool_id, and its schema description already explains it ('Tool id (from list_tools).'). The tool description adds no additional meaning or constraints beyond the schema. Since schema_description_coverage is 100%, baseline is 3. The description does not elaborate on format, source, or validation, so it meets but does not exceed the baseline.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: given a tool id, return the tool info and all its rankings. It specifies the output structure (tool info and array of ranking objects). This distinguishes it from sibling tools like 'get_ranking' (which fetches a single ranking) and 'tools_in_ranking' (which fetches tools for a ranking). The verb 'return' and specific resource 'every ranking it appears in' make the purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage context (when you need rankings for a specific tool), but it does not provide explicit when-to-use or when-not-to-use guidance. No alternatives are mentioned, such as using 'tools_in_ranking' for the inverse relationship. The agent must infer from sibling names, which may not be sufficient. A 3 reflects implied but not explicit guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full burden. Describes return format (ranked light refs with fields) and mode behaviors (substring vs meaning vs hybrid). Does not mention state mutation, auth, or rate limits, but as a search tool these are not critical. Missing pagination details beyond limit.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with purpose and result structure. Efficiently covers all key aspects without wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers purpose, result format, mode behavior, and filter options. With no output schema, describing return structure is critical and done well. Missing offset/pagination details but limit is specified. Adequate for a search tool with clear sibling tools for detail retrieval.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema has 100% coverage, but description adds value: explains what each mode does in plain language (e.g., semantic uses embeddings), defaults (limit 20), and that type filters restrict to kinds. Enhances understanding beyond schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states it searches a published catalogue across categories (use cases, rankings, etc.) and returns ranked light refs. Distinguishes from siblings by noting subsequent calls to get_tool, get_ranking, etc. for full detail.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly tells when to use (search catalogue) and what to do after (call specific get_* tools). Explains three search modes (keyword, semantic, hybrid) and optional filters (type, persona, category), guiding appropriate usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, and the description does not disclose any behavioral traits (e.g., read-only nature, authentication needs, rate limits). The burden is fully on the description, which is insufficient.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence that efficiently communicates the core functionality and return format, though it could be slightly more structured with bullet points.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple query tool with one parameter and no output schema, the description explains the return structure and even lists possible badge values, providing adequate context for the agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with a well-described parameter ranking_id. The description adds no additional meaning beyond the schema, so it meets the baseline of 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description specifies the tool returns ranking fields (id, title, slug) and an array of tools with detailed fields (id, name, slug, url, rank, badge), clearly distinguishing it from siblings like get_ranking or list_rankings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage when you have a ranking_id and want tools, but lacks explicit guidance on when not to use it or mention of alternatives such as get_ranking or rankings_for_tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.